The present invention relates to a method and apparatus for analyzing waveform signals of a power system.
In recent years, advancements in microprocessor-based devices and digital signal processing technologies combined with increased emphasis on power system reliability and online monitoring have led to the proliferation of various types of Intelligent Electronic Devices (IEDs) that record and store digital waveforms along with other data. The recorded data are primarily utilized for protection and control applications, but there is abundant information embedded in the raw measurements that are seldom utilized regularly.
For instance, a current waveform captured before, during, and after a fault or an event by an IED contains unique information for monitoring, identification, fault location, and classification purposes. The information extracted from these measurements may provide invaluable insights into the type and extent of the fault, which helps to better plan and prepare for the remedial actions by the maintenance crew in the field. This is traditionally performed by a trained individual on limited cases. The net effect of using this information partially includes direct and indirect reduction in the operation and maintenance costs.
Most electric utilities record field data, operational and non-operational, but the overwhelming nature of manual data analysis, lack of human resources, and inevitable inconsistencies preclude delivering meaningful information to the operation and maintenance crew. As a result, a substantial portion of these useful measurements, mostly non-operational, is often abandoned and never utilized.
Some automated analyses of digital waveforms signals have been proposed in the prior art dealing in particular with the difficult tasks involved in the front-end processing, namely waveform segmentation and classification of the segments into predetermined classes.
Many segmentation methods in the prior art are based on Kalman filters. The filter is employed to model the waveform and the residuals are used for segmentation. In common with other model-based approaches, the response of the filter is affected by the accuracy of the model, its parameters, and settings. Also, the detection capability of the model is a function of the magnitude of the change, harmonic contents, and other frequency components not modeled in the designed filter. Although the filtering approach has been used in some model-based applications, it has some drawbacks for online and IED applications. The Kalman filter is computationally expensive, and limited by the accuracy of the model it represents. Furthermore, fine-tuning the filter parameters is cumbersome and requires prior hard-to-find information in many practical cases.
Multi-resolution signal decomposition methods based on wavelets are also proposed for modeling and analysis purposes. The information obtained at different resolution levels and the measured values are used to analyze signal segments. Designed primarily for monitoring power quality problems, wavelet-based approaches are not suitable for embedded applications due in part to the extensive computational and storage requirements.
It would be therefore desirable to provide a solution for analyzing waveform signals in power systems that does not require modeling and can be implemented in IEDs and executed online or offline in computerized applications. This solution is provided by the method and apparatus of the present invention.